In [1]:
!pip install pandas==1.3.3
!pip install requests==2.26.0
!pip install bs4==4.10.0 -y
!pip install html5lib==1.1 -y
!pip install lxml==4.6.4
!pip install plotly==5.3.1

Collecting pandas==1.3.3
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no such option: -y

Usage:   
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In [4]:
!pip install yfinance

Collecting yfinance
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Successfully built multitasking
Installing collected packages: multitasking, yfinance
Successfully installed multitasking-0.0.10 yfinance-0.1.72
In [2]:
import yfinance as yf
import pandas as pd
import requests
from bs4 import BeautifulSoup
import plotly.graph_objects as go
from plotly.subplots import make_subplots
In [12]:
def make_graph(stock_data, revenue_data, stock):
    fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Historical Share Price", "Historical Revenue"), vertical_spacing = .3)
    stock_data_specific = stock_data[stock_data.Date <= '2021--06-14']
    revenue_data_specific = revenue_data[revenue_data.Date <= '2021-04-30']
    fig.add_trace(go.Scatter(x=pd.to_datetime(stock_data_specific.Date, infer_datetime_format=True), y=stock_data_specific.Close.astype("float"), name="Share Price"), row=1, col=1)
    fig.add_trace(go.Scatter(x=pd.to_datetime(revenue_data_specific.Date, infer_datetime_format=True), y=revenue_data_specific.Revenue.astype("float"), name="Revenue"), row=2, col=1)
    fig.update_xaxes(title_text="Date", row=1, col=1)
    fig.update_xaxes(title_text="Date", row=2, col=1)
    fig.update_yaxes(title_text="Price ($US)", row=1, col=1)
    fig.update_yaxes(title_text="Revenue ($US Millions)", row=2, col=1)
    fig.update_layout(showlegend=False,
    height=900,
    title=stock,
    xaxis_rangeslider_visible=True)
    fig.show()

q1_tsla_share_price¶

In [4]:
tsla = yf.Ticker('TSLA')
In [5]:
tsla_info = tsla.info
In [6]:
tsla_price = tsla.history(period='max')
In [7]:
tsla_price.reset_index(inplace=True)
In [25]:
tsla_price.head
Out[25]:
<bound method NDFrame.head of            Date        Open        High         Low       Close    Volume  \
0    2010-06-29    3.800000    5.000000    3.508000    4.778000  93831500   
1    2010-06-30    5.158000    6.084000    4.660000    4.766000  85935500   
2    2010-07-01    5.000000    5.184000    4.054000    4.392000  41094000   
3    2010-07-02    4.600000    4.620000    3.742000    3.840000  25699000   
4    2010-07-06    4.000000    4.000000    3.166000    3.222000  34334500   
...         ...         ...         ...         ...         ...       ...   
3010 2022-06-13  669.500000  679.900024  644.049988  647.210022  34255800   
3011 2022-06-14  654.859985  678.989990  635.210022  662.669983  32662900   
3012 2022-06-15  662.750000  706.989990  654.450012  699.000000  39710600   
3013 2022-06-16  668.210022  675.500000  626.080017  639.299988  35796900   
3014 2022-06-17  640.299988  662.909973  639.590027  650.280029  30810900   

      Dividends  Stock Splits  
0             0           0.0  
1             0           0.0  
2             0           0.0  
3             0           0.0  
4             0           0.0  
...         ...           ...  
3010          0           0.0  
3011          0           0.0  
3012          0           0.0  
3013          0           0.0  
3014          0           0.0  

[3015 rows x 8 columns]>

q2_TESLA_revenue¶

In [13]:
url = 'https://www.macrotrends.net/stocks/charts/TSLA/tesla/revenue'
r = requests.get(url).text
tsla_soup = BeautifulSoup(r,'html.parser')
tesla_revenue = pd.DataFrame(columns=['Date', 'Revenue'])
rows = tsla_soup.find_all('tbody')[1].find_all('td')
tsla_date = []
tsla_rev =[]

for i, row in enumerate(rows):
    if i % 2 == 0:
        tsla_date.append(row.text)
    else:
        tsla_rev.append(row.text)
tesla_revenue['Date'] = tsla_date
tesla_revenue['Revenue'] = tsla_rev
tesla_revenue
Out[13]:
Date Revenue
0 2022-03-31 $18,756
1 2021-12-31 $17,719
2 2021-09-30 $13,757
3 2021-06-30 $11,958
4 2021-03-31 $10,389
5 2020-12-31 $10,744
6 2020-09-30 $8,771
7 2020-06-30 $6,036
8 2020-03-31 $5,985
9 2019-12-31 $7,384
10 2019-09-30 $6,303
11 2019-06-30 $6,350
12 2019-03-31 $4,541
13 2018-12-31 $7,226
14 2018-09-30 $6,824
15 2018-06-30 $4,002
16 2018-03-31 $3,409
17 2017-12-31 $3,288
18 2017-09-30 $2,985
19 2017-06-30 $2,790
20 2017-03-31 $2,696
21 2016-12-31 $2,285
22 2016-09-30 $2,298
23 2016-06-30 $1,270
24 2016-03-31 $1,147
25 2015-12-31 $1,214
26 2015-09-30 $937
27 2015-06-30 $955
28 2015-03-31 $940
29 2014-12-31 $957
30 2014-09-30 $852
31 2014-06-30 $769
32 2014-03-31 $621
33 2013-12-31 $615
34 2013-09-30 $431
35 2013-06-30 $405
36 2013-03-31 $562
37 2012-12-31 $306
38 2012-09-30 $50
39 2012-06-30 $27
40 2012-03-31 $30
41 2011-12-31 $39
42 2011-09-30 $58
43 2011-06-30 $58
44 2011-03-31 $49
45 2010-12-31 $36
46 2010-09-30 $31
47 2010-06-30 $28
48 2010-03-31 $21
49 2009-12-31
50 2009-09-30 $46
51 2009-06-30 $27
In [15]:
tesla_revenue["Revenue"] = tesla_revenue['Revenue'].str.replace(',|\$',"")

/var/folders/jb/2n_fl39j35g_18r59zrbs9bm0000gn/T/ipykernel_618/349343550.py:1: FutureWarning:

The default value of regex will change from True to False in a future version.
In [16]:
tesla_revenue.dropna(inplace=True)

tesla_revenue = tesla_revenue[tesla_revenue['Revenue'] != ""]

q3_gme_stock_data¶

In [24]:
gme = yf.Ticker('GME')
gme_data = gme.history(period='max')
gme_data.reset_index(inplace=True)
gme_data
Out[24]:
Date Open High Low Close Volume Dividends Stock Splits
0 2002-02-13 6.480513 6.773399 6.413183 6.766665 19054000 0.0 0.0
1 2002-02-14 6.850829 6.864295 6.682504 6.733002 2755400 0.0 0.0
2 2002-02-15 6.733001 6.749833 6.632006 6.699336 2097400 0.0 0.0
3 2002-02-19 6.665671 6.665671 6.312189 6.430017 1852600 0.0 0.0
4 2002-02-20 6.463681 6.648838 6.413183 6.648838 1723200 0.0 0.0
... ... ... ... ... ... ... ... ...
5118 2022-06-13 120.510002 124.570000 114.300003 118.250000 3448200 0.0 0.0
5119 2022-06-14 117.570000 128.000000 116.099998 126.169998 3184800 0.0 0.0
5120 2022-06-15 124.949997 131.960007 123.639999 129.289993 2691100 0.0 0.0
5121 2022-06-16 124.940002 129.289993 120.580002 125.730003 2498800 0.0 0.0
5122 2022-06-17 126.860001 135.860001 126.320000 135.139999 3080200 0.0 0.0

5123 rows × 8 columns

q4_gme_revenue¶

In [45]:
gme_url = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html'
In [56]:
r_gme = requests.get(gme_url)
In [63]:
gme_soup = BeautifulSoup(r_gme.text, 'html.parser')
gme_rows = gme_soup.find_all('tbody')[1].find_all('td')

gme_df = pd.DataFrame(columns = ['Date', 'Revenue'])
gme_date = []
gme_rev = []

for i,row in enumerate(gme_rows):
    if i % 2 == 0:
#         print(row.text)
        gme_date.append(row.text)
    else:
#         print(row.text)
        gme_rev.append(row.text)
gme_df['Date'] = gme_date
gme_df['Revenue'] = gme_rev
gme_df
Out[63]:
Date Revenue
0 2020-04-30 $1,021
1 2020-01-31 $2,194
2 2019-10-31 $1,439
3 2019-07-31 $1,286
4 2019-04-30 $1,548
... ... ...
57 2006-01-31 $1,667
58 2005-10-31 $534
59 2005-07-31 $416
60 2005-04-30 $475
61 2005-01-31 $709

62 rows × 2 columns

In [65]:
gme_df["Revenue"] = gme_df['Revenue'].str.replace(',|\$',"")

gme_df.dropna(inplace=True)

gme_df = gme_df[gme_df['Revenue'] != ""]

/var/folders/jb/2n_fl39j35g_18r59zrbs9bm0000gn/T/ipykernel_618/2197658718.py:1: FutureWarning:

The default value of regex will change from True to False in a future version.

q5_tsla_graph¶

In [17]:
make_graph(tsla_price, tesla_revenue, 'Tesla')

q6_gme_graph¶

In [66]:
make_graph(gme_data, gme_df, 'GME')